Tom Sosnoff disagrees with an ad in Barron's that says, "You can't predict the future, but you can prepare for it." What do you think?

published:23 Jul 2013

views:281

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its top 20 positions unfilled and that has been having difficulty recruiting the most talented women because of some of the cultural issues.
And when your talent is not growing at the same rate as your revenues breakdowns begin to happen.
A second element of the west winds is what we call lack of accountability. And you can look at the culture as it evolved in Uber, which has been well reported, and say, “who is really accountable for the norms and values—deep, soulful values—of a company?” And it’s very easy to neglect that in the context of growth and hype and excitement and celebrity.
Or a third one, or a third of these winds is what we called lost voices from the frontline. And if you look at, for example, the loyalty numbers that you can find all over the internet of Lyft versus Uber drivers, what you find is that the loyalty of Uber drivers is going down, because they’re a little bit frustrated with a lot of the publicity and with some of the behaviors that they’ve observed, and they’re more disoriented about the company.
And so these west winds hit rapidly growing companies. And what we found is that when we looked at these unicorns, we took 26 unicorns about 10 years ago, 10 to 12 years ago, and we traced them. And we found that virtually 100 percent of them—and two-thirds of them had slowed down dramatically and never hit what people wanted them to. Uber would be an example.
And second, we found that in virtually all of the cases the deep inner root cause was not that it was a bad idea in the first place or the market had gone away; It was actually inner breakdowns like this case.
And all of these linked to the founder’s mentality, 1) of linkage to the frontline, 2) maintaining a clear purpose versus overcomplicating what you’re trying to do and becoming greedy and doing too much, and 3) creating mini founder experiences that make people really want to become part of it.
The second crisis is the more predominant crisis, and it’s what we call a stall-out.
So if you think of a company, let’s say like Lego, which since the 1930s was a great founder company through three or four generations. The first did it in wooden blocks. The next brought it to plastic. The next created the business systems. And it was voted the toy of the year by the British toy industry.
And yet the next generation began to say “No, what our core is is the brand. We’re not a toy company.”
And so they went into many, many things that massively complicated the business from theme parks to joint ventures with Steven Spielberg in small theaters to retail endeavors to plastic watches to books, and on and on and on.
And it sucked the energy out of the core and resulted in stall-out to a point where the company had 18 months of cash left.
And the solution to that was to massively reduce complexity, exit all those businesses (by the CEO who courageously went in, Jorgen Vig Knudstorp, well reported in the press) and it gave the company 12 years.
He brought technology into the bricks.
He found out who the core customers were. They didn’t know there were 400,000 people who are obsessed with Lego, they brought them into the design process.
And they did many, many things to actually go back to the essence of what made the company great in the first place, which was the mission of learning, and the customer desire for toy systems that would help children creatively.
And they even did things like take the corporate headquarters (which was in a bright, gleaming new building) and brought it back into the factory and the distribution center.
So that’s an example of stall-out, and that’s an example of a company that then had 12 years of very, very good growth as a result of, in a sense, finding the key to it—where the Founder’s Mentality elements were part of the playbook.

Use the link below to see my Guide to GCSEEnglish Language
https://goo.gl/rmdQul
Every year students ask for predictions. This video shows you how to get it right every time.

published:08 May 2016

views:13295

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its top 20 positions unfilled and that has been having difficulty recruiting the most talented women because of some of the cultural issues.
And when your talent is not growing at the same rate as your revenues breakdowns begin to happen.
A second element of the west winds is what we call lack of accountability. And you can look at the culture as it evolved in Uber, which has been well reported, and say, “who is really accountable for the norms and values—deep, soulful values—of a company?” And it’s very easy to neglect that in the context of growth and hype and excitement and celebrity.
Or a third one, or a third of these winds is what we called lost voices from the frontline. And if you look at, for example, the loyalty numbers that you can find all over the internet of Lyft versus Uber drivers, what you find is that the loyalty of Uber drivers is going down, because they’re a little bit frustrated with a lot of the publicity and with some of the behaviors that they’ve observed, and they’re more disoriented about the company.
And so these west winds hit rapidly growing companies. And what we found is that when we looked at these unicorns, we took 26 unicorns about 10 years ago, 10 to 12 years ago, and we traced them. And we found that virtually 100 percent of them—and two-thirds of them had slowed down dramatically and never hit what people wanted them to. Uber would be an example.
And second, we found that in virtually all of the cases the deep inner root cause was not that it was a bad idea in the first place or the market had gone away; It was actually inner breakdowns like this case.
And all of these linked to the founder’s mentality, 1) of linkage to the frontline, 2) maintaining a clear purpose versus overcomplicating what you’re trying to do and becoming greedy and doing too much, and 3) creating mini founder experiences that make people really want to become part of it.
The second crisis is the more predominant crisis, and it’s what we call a stall-out.
So if you think of a company, let’s say like Lego, which since the 1930s was a great founder company through three or four generations. The first did it in wooden blocks. The next brought it to plastic. The next created the business systems. And it was voted the toy of the year by the British toy industry.
And yet the next generation began to say “No, what our core is is the brand. We’re not a toy company.”
And so they went into many, many things that massively complicated the business from theme parks to joint ventures with Steven Spielberg in small theaters to retail endeavors to plastic watches to books, and on and on and on.
And it sucked the energy out of the core and resulted in stall-out to a point where the company had 18 months of cash left.
And the solution to that was to massively reduce complexity, exit all those businesses (by the CEO who courageously went in, Jorgen Vig Knudstorp, well reported in the press) and it gave the company 12 years.
He brought technology into the bricks.
He found out who the core customers were. They didn’t know there were 400,000 people who are obsessed with Lego, they brought them into the design process.
And they did many, many things to actually go back to the essence of what made the company great in the first place, which was the mission of learning, and the customer desire for toy systems that would help children creatively.
And they even did things like take the corporate headquarters (which was in a bright, gleaming new building) and brought it back into the factory and the distribution center.
So that’s an example of stall-out, and that’s an example of a company that then had 12 years of very, very good growth as a result of, in a sense, finding the key to it—where the Founder’s Mentality elements were part of the playbook.

How to Predict Your Literature Exam Question!

Use the link below to see my Guide to GCSEEnglish Language
https://goo.gl/rmdQul
Every year students ask for predictions. This video shows you how to get it right every time.

6:48

Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)

Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)

Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Can Lenormand be used to predict gambling outcomes?

The Lenormand system is a highly accurate tool to for predicting....so can it also be used to predict gambling outcomes? Here is information on what I've learned.

0:47

What Does The Word Predict?

What Does The Word Predict?

What Does The Word Predict?

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in our database the word 'predict' example sentences page 1. It is often, but not always, albert einstein's theory of general relativity could easily be tested as it did produce any effects observable on a terrestrial scale. Word prediction ghotit. Manythings sentences words predict tom did a good job predicting who would win the election ghotit quick spell word prediction helps people with dyslexia and even if topic does not add single new to dictionary, will. Word origin a prediction is what someone thinks will happen. It's a guess, sometimes based on facts or evidence, but not always define predict to say that (something) will might happen in the future words from latin dicere have something do with saying speaking meaning, definition, what is an event action question word ] no one can when disease strike again sentence. However, the 23 dec 2015 if so, then take a look at our collection of predict synonyms. Definition of predict by merriam webster. Predicting and prediction synonyms related words kids. The word 'predict' in example sentences page 1. Au dictionary definition prediction. Predict meaning in the cambridge english dictionary. Pre means before and diction has to do with talking. Predict, prophesy, foresee, forecast mean to know or tell (usually correctly) beforehand what will happen predicting definition, declare in advanceforetell last year, like most sports fans, he did so the winners. Rank popularity for the word 'predict' in verbs frequency #495 predict definition if you an event, say that it will happen. So a prediction is statement about the future. Example sentences with the word predictthey do not represent opinions of yourdictionary. Use predict in a sentence what does mean? Definitions collins english dictionaryoxfordwords blog. Define predict at dictionary browse url? Q webcache. Word forms 3rd person singular present tense predicts, participle predicting a prediction or forecast, is statement about an uncertain event. Prediction dictionary definition vocabulary. A prediction is a forecast, but not only about the weather. Mind reading tricks for the office how to do a word prediction definition math glossary from mathgoodies. Link cite definition of predict in the definitions dictionaryverbs frequency. Do you know how the new year is going to turn out? With tv weatherpersons (just kidding, meteorologists!), word forecast another fine synonym for predict i am not sure if could use in context of a scientific prediction simply saying that something will happen future an 'intelligent' processing feature can alleviate writing since her target did appear this list, student selects next comprehensive list synonyms predicting and prediction, by macmillan dictionary thesaurus reacts ways parents sensitivity rate describes ability ofthe model correctly positives what do next? T

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output ...

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its t...

published: 09 Dec 2017

PREDICT THE FUTURE TWICE | PigCake Tutorials

DO YOU WANT TO LEARN A CLEVER AND INTERESTING SLEIGHT YOU CAN APPLY TO OTHER TRICKS TO FACILITATE SOCIAL INTERACTION WITH OTHER PEOPLE???? DO YOU WANT TO BEABLE TO PREDICT THE FUTURE TWICE IN THE CONTEXT OF ONE TRICK? ARE YOU TIRED OF READING DESCRIPTIONS TO YOUTUBE VIDEOS?
ACAAN Project: http://www.pigcake.me
Support me on Patreon: https://www.patreon.com/PigCakee
Follow me on Twitter: www.twitter.com/PigCakee
PO Box 140307
Coral Gables, Florida 33114
Send some hot memes you want opened on camera
Deck I used:
Discord Chat : https://discord.gg/qTdvHcH
Shit I Use to film (love tech but only need what gets the job done):
Cameras:
MainCamera: http://amzn.to/2nf2awP
Secondary Camera: http://amzn.to/2nMo1i2
Mics:
Snowball: http://amzn.to/2nw2IRi
Videomic GO: http://amzn.to/2mZp1e6
Vid...

published: 28 Aug 2017

How to Make a Text Summarizer - Intro to Deep Learning #10

I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory.
Code for this video (Challenge included):
https://github.com/llSourcell/How_to_make_a_text_summarizer
Jie's Winning Code:
https://github.com/jiexunsee/rudimentary-ai-composer
More Learning resources:
https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully
https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
https://en.wikipedia.org/wiki/Automatic_summarization
http://deeplearning.net/tutorial/rnnslu.html
http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-kera...

published: 17 Mar 2017

OSHO: Oracles, Tarot and Other Divination Tools

OSHO: Oracles, Tarot and Other DivinationTools
OSHO ZENTAROT: http://osho.com/zentarot
OSHO TRANSFORMATION TAROT: http://goo.gl/yrcQyX
Video: http://.youtube.com/watch?v=zeo6ZJ7WCjY
Although Osho was not at all a believer or supporter of future predictions of any kind, he responded to a number of questions regarding oracles, astrology, the i-ching and the tarot. Leaving behind all superstition Osho addresses the real underlying issues of the questions. The Osho Zen Tarot to which Osho gave the name was created not as a tool to predict the future but to understand ourselves -- here-now. In this way it is used today by millions of people in all formats it is available.
The OSHO Zen Tarot is available:
OSHO Zen Tarot for iPhone : https://goo.gl/lh7DZu
OSHO Zen Tarot for Android: http...

published: 16 Mar 2015

Economic Forecasting: How to Predict the Future

http://www.patrickschwerdtfeger.com/sbi/
Patrick Schwerdtfeger discusses the impact of demographics on future economic growth for countries around the world including America, Latin America, Europe, Russia, China, Japan, India, Pakistan and the Middle East and North Africa. In particular, he looks at population growth, the age profile and net exports as a way to predict expected economic growth between 2010 and 2050.
This type of analysis is extremely valuable for people interested in international business. Their interest might be motivated by business interests, international investment objectives or thought leadership around the world. Regardless how you use this information, it will allow you to literally predict the future for the economic performance of countries all around the ...

published: 17 Jan 2013

How to Predict Your Literature Exam Question!

Use the link below to see my Guide to GCSEEnglish Language
https://goo.gl/rmdQul
Every year students ask for predictions. This video shows you how to get it right every time.

published: 08 May 2016

Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While th...

published: 19 Jul 2016

Alex Jones Predicted Vegas Attack On Friday

On the Friday edition ofThe Alex Jones Show, Alex predicted that early October the revolution would start and build up to Nov 4th.
Full hour coming soon.
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published: 02 Oct 2017

Can Lenormand be used to predict gambling outcomes?

The Lenormand system is a highly accurate tool to for predicting....so can it also be used to predict gambling outcomes? Here is information on what I've learned.

published: 08 May 2017

What Does The Word Predict?

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in our database the word 'predict' example sentences page 1. It is often, but not always, albert einstein's theory of general relativity could easily be tested as it did produce any effects observable on a terrestrial scale. Word prediction ghotit. Manythings sentences words predict tom did a good job predicting who would win the election ghotit quick spell word prediction helps people with dyslexia and even if topic does not add single new to dictionary, will. Word origin a prediction is what someone thinks will happen. It's a guess, sometimes based on facts or evidence, but not always define predict to say that (something) will might...

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years af...

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its top 20 positions unfilled and that has been having difficulty recruiting the most talented women because of some of the cultural issues.
And when your talent is not growing at the same rate as your revenues breakdowns begin to happen.
A second element of the west winds is what we call lack of accountability. And you can look at the culture as it evolved in Uber, which has been well reported, and say, “who is really accountable for the norms and values—deep, soulful values—of a company?” And it’s very easy to neglect that in the context of growth and hype and excitement and celebrity.
Or a third one, or a third of these winds is what we called lost voices from the frontline. And if you look at, for example, the loyalty numbers that you can find all over the internet of Lyft versus Uber drivers, what you find is that the loyalty of Uber drivers is going down, because they’re a little bit frustrated with a lot of the publicity and with some of the behaviors that they’ve observed, and they’re more disoriented about the company.
And so these west winds hit rapidly growing companies. And what we found is that when we looked at these unicorns, we took 26 unicorns about 10 years ago, 10 to 12 years ago, and we traced them. And we found that virtually 100 percent of them—and two-thirds of them had slowed down dramatically and never hit what people wanted them to. Uber would be an example.
And second, we found that in virtually all of the cases the deep inner root cause was not that it was a bad idea in the first place or the market had gone away; It was actually inner breakdowns like this case.
And all of these linked to the founder’s mentality, 1) of linkage to the frontline, 2) maintaining a clear purpose versus overcomplicating what you’re trying to do and becoming greedy and doing too much, and 3) creating mini founder experiences that make people really want to become part of it.
The second crisis is the more predominant crisis, and it’s what we call a stall-out.
So if you think of a company, let’s say like Lego, which since the 1930s was a great founder company through three or four generations. The first did it in wooden blocks. The next brought it to plastic. The next created the business systems. And it was voted the toy of the year by the British toy industry.
And yet the next generation began to say “No, what our core is is the brand. We’re not a toy company.”
And so they went into many, many things that massively complicated the business from theme parks to joint ventures with Steven Spielberg in small theaters to retail endeavors to plastic watches to books, and on and on and on.
And it sucked the energy out of the core and resulted in stall-out to a point where the company had 18 months of cash left.
And the solution to that was to massively reduce complexity, exit all those businesses (by the CEO who courageously went in, Jorgen Vig Knudstorp, well reported in the press) and it gave the company 12 years.
He brought technology into the bricks.
He found out who the core customers were. They didn’t know there were 400,000 people who are obsessed with Lego, they brought them into the design process.
And they did many, many things to actually go back to the essence of what made the company great in the first place, which was the mission of learning, and the customer desire for toy systems that would help children creatively.
And they even did things like take the corporate headquarters (which was in a bright, gleaming new building) and brought it back into the factory and the distribution center.
So that’s an example of stall-out, and that’s an example of a company that then had 12 years of very, very good growth as a result of, in a sense, finding the key to it—where the Founder’s Mentality elements were part of the playbook.

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its top 20 positions unfilled and that has been having difficulty recruiting the most talented women because of some of the cultural issues.
And when your talent is not growing at the same rate as your revenues breakdowns begin to happen.
A second element of the west winds is what we call lack of accountability. And you can look at the culture as it evolved in Uber, which has been well reported, and say, “who is really accountable for the norms and values—deep, soulful values—of a company?” And it’s very easy to neglect that in the context of growth and hype and excitement and celebrity.
Or a third one, or a third of these winds is what we called lost voices from the frontline. And if you look at, for example, the loyalty numbers that you can find all over the internet of Lyft versus Uber drivers, what you find is that the loyalty of Uber drivers is going down, because they’re a little bit frustrated with a lot of the publicity and with some of the behaviors that they’ve observed, and they’re more disoriented about the company.
And so these west winds hit rapidly growing companies. And what we found is that when we looked at these unicorns, we took 26 unicorns about 10 years ago, 10 to 12 years ago, and we traced them. And we found that virtually 100 percent of them—and two-thirds of them had slowed down dramatically and never hit what people wanted them to. Uber would be an example.
And second, we found that in virtually all of the cases the deep inner root cause was not that it was a bad idea in the first place or the market had gone away; It was actually inner breakdowns like this case.
And all of these linked to the founder’s mentality, 1) of linkage to the frontline, 2) maintaining a clear purpose versus overcomplicating what you’re trying to do and becoming greedy and doing too much, and 3) creating mini founder experiences that make people really want to become part of it.
The second crisis is the more predominant crisis, and it’s what we call a stall-out.
So if you think of a company, let’s say like Lego, which since the 1930s was a great founder company through three or four generations. The first did it in wooden blocks. The next brought it to plastic. The next created the business systems. And it was voted the toy of the year by the British toy industry.
And yet the next generation began to say “No, what our core is is the brand. We’re not a toy company.”
And so they went into many, many things that massively complicated the business from theme parks to joint ventures with Steven Spielberg in small theaters to retail endeavors to plastic watches to books, and on and on and on.
And it sucked the energy out of the core and resulted in stall-out to a point where the company had 18 months of cash left.
And the solution to that was to massively reduce complexity, exit all those businesses (by the CEO who courageously went in, Jorgen Vig Knudstorp, well reported in the press) and it gave the company 12 years.
He brought technology into the bricks.
He found out who the core customers were. They didn’t know there were 400,000 people who are obsessed with Lego, they brought them into the design process.
And they did many, many things to actually go back to the essence of what made the company great in the first place, which was the mission of learning, and the customer desire for toy systems that would help children creatively.
And they even did things like take the corporate headquarters (which was in a bright, gleaming new building) and brought it back into the factory and the distribution center.
So that’s an example of stall-out, and that’s an example of a company that then had 12 years of very, very good growth as a result of, in a sense, finding the key to it—where the Founder’s Mentality elements were part of the playbook.

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

What Does The Word Predict?

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in ...

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in our database the word 'predict' example sentences page 1. It is often, but not always, albert einstein's theory of general relativity could easily be tested as it did produce any effects observable on a terrestrial scale. Word prediction ghotit. Manythings sentences words predict tom did a good job predicting who would win the election ghotit quick spell word prediction helps people with dyslexia and even if topic does not add single new to dictionary, will. Word origin a prediction is what someone thinks will happen. It's a guess, sometimes based on facts or evidence, but not always define predict to say that (something) will might happen in the future words from latin dicere have something do with saying speaking meaning, definition, what is an event action question word ] no one can when disease strike again sentence. However, the 23 dec 2015 if so, then take a look at our collection of predict synonyms. Definition of predict by merriam webster. Predicting and prediction synonyms related words kids. The word 'predict' in example sentences page 1. Au dictionary definition prediction. Predict meaning in the cambridge english dictionary. Pre means before and diction has to do with talking. Predict, prophesy, foresee, forecast mean to know or tell (usually correctly) beforehand what will happen predicting definition, declare in advanceforetell last year, like most sports fans, he did so the winners. Rank popularity for the word 'predict' in verbs frequency #495 predict definition if you an event, say that it will happen. So a prediction is statement about the future. Example sentences with the word predictthey do not represent opinions of yourdictionary. Use predict in a sentence what does mean? Definitions collins english dictionaryoxfordwords blog. Define predict at dictionary browse url? Q webcache. Word forms 3rd person singular present tense predicts, participle predicting a prediction or forecast, is statement about an uncertain event. Prediction dictionary definition vocabulary. A prediction is a forecast, but not only about the weather. Mind reading tricks for the office how to do a word prediction definition math glossary from mathgoodies. Link cite definition of predict in the definitions dictionaryverbs frequency. Do you know how the new year is going to turn out? With tv weatherpersons (just kidding, meteorologists!), word forecast another fine synonym for predict i am not sure if could use in context of a scientific prediction simply saying that something will happen future an 'intelligent' processing feature can alleviate writing since her target did appear this list, student selects next comprehensive list synonyms predicting and prediction, by macmillan dictionary thesaurus reacts ways parents sensitivity rate describes ability ofthe model correctly positives what do next? T

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in our database the word 'predict' example sentences page 1. It is often, but not always, albert einstein's theory of general relativity could easily be tested as it did produce any effects observable on a terrestrial scale. Word prediction ghotit. Manythings sentences words predict tom did a good job predicting who would win the election ghotit quick spell word prediction helps people with dyslexia and even if topic does not add single new to dictionary, will. Word origin a prediction is what someone thinks will happen. It's a guess, sometimes based on facts or evidence, but not always define predict to say that (something) will might happen in the future words from latin dicere have something do with saying speaking meaning, definition, what is an event action question word ] no one can when disease strike again sentence. However, the 23 dec 2015 if so, then take a look at our collection of predict synonyms. Definition of predict by merriam webster. Predicting and prediction synonyms related words kids. The word 'predict' in example sentences page 1. Au dictionary definition prediction. Predict meaning in the cambridge english dictionary. Pre means before and diction has to do with talking. Predict, prophesy, foresee, forecast mean to know or tell (usually correctly) beforehand what will happen predicting definition, declare in advanceforetell last year, like most sports fans, he did so the winners. Rank popularity for the word 'predict' in verbs frequency #495 predict definition if you an event, say that it will happen. So a prediction is statement about the future. Example sentences with the word predictthey do not represent opinions of yourdictionary. Use predict in a sentence what does mean? Definitions collins english dictionaryoxfordwords blog. Define predict at dictionary browse url? Q webcache. Word forms 3rd person singular present tense predicts, participle predicting a prediction or forecast, is statement about an uncertain event. Prediction dictionary definition vocabulary. A prediction is a forecast, but not only about the weather. Mind reading tricks for the office how to do a word prediction definition math glossary from mathgoodies. Link cite definition of predict in the definitions dictionaryverbs frequency. Do you know how the new year is going to turn out? With tv weatherpersons (just kidding, meteorologists!), word forecast another fine synonym for predict i am not sure if could use in context of a scientific prediction simply saying that something will happen future an 'intelligent' processing feature can alleviate writing since her target did appear this list, student selects next comprehensive list synonyms predicting and prediction, by macmillan dictionary thesaurus reacts ways parents sensitivity rate describes ability ofthe model correctly positives what do next? T

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output ...

published: 24 Jul 2017

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts...

published: 04 Jun 2017

Economic Forecasting: How to Predict the Future

http://www.patrickschwerdtfeger.com/sbi/
Patrick Schwerdtfeger discusses the impact of demographics on future economic growth for countries around the world including America, Latin America, Europe, Russia, China, Japan, India, Pakistan and the Middle East and North Africa. In particular, he looks at population growth, the age profile and net exports as a way to predict expected economic growth between 2010 and 2050.
This type of analysis is extremely valuable for people interested in international business. Their interest might be motivated by business interests, international investment objectives or thought leadership around the world. Regardless how you use this information, it will allow you to literally predict the future for the economic performance of countries all around the ...

published: 17 Jan 2013

Qur'an in Context 1: "Fight Those Who Do Not Believe" (9:29)

Support our videos on Patreon: https://www.patreon.com/user?u=3615911
http://www.answeringmuslims.com/p/jihad.htmlThe Qur'an is filled with violent passages. Yet Muslims assure us that these passages, when read in context, are peaceful. In this video, we examine the historical, immediate, and extended literary contexts of Surah 9:29, which commands Muslims to "fight those who do not believe in Allah."

published: 20 May 2012

Module 14 - Using Markers to Predict Breeding Values

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN). This module focuses on prediction of breeding values using molecular markers in the context of forest trees.

How to predict students' performance?

Talk presented at SSCI2014, in Orlando.
Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdfAbstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariat...

published: 15 Dec 2014

How to Predict Market Direction using Volume and Price | Nigel Hawks

How to get paid to figure out what kind of product or program to sell online
How to create a movement that naturally births a product that everyone wants to buy
How to turn people’s questions, comments, frustrations, and feedback into solid income
How to determine whether a future product will produce the money you need and deserve
InvestorInspiration delivers unbiased investment information by providing a platform for top tier investors to both educate you and inform you about their products. Our primary method of delivering investment information is through webinars featuring multiple industry leading speakers. Find your inspiration today by joining us in our next live webinar or viewing one of our on demand webinar sessions.

published: 07 Apr 2016

The Trump Presidency: Last Week Tonight with John Oliver (HBO)

One year after the presidential election, John Oliver discusses what we've learned so far and enlists our catheter cowboy to teach Donald Trump what he hasn't.
Connect with Last Week Tonight online...
Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight
Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight
Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight
Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight

Estimating Impact - How can we predict the variants most likely to have an effect...

Webinar: How to use open types and volatility to predict potential trading ranges #marketprofile

Mentorships for those who have the drive to learn to trade patiently and slowly. https://marketprofiletradingacademy.com/were-having-a-blast-with-slack-come-join-us/
Call us 541-728-3217 or set 30 minute free consult
Trading is difficult and it can be fun if you can gather yourself and remove your ego and be patient in the process of the market auction coming to you! Time over Price
https://marketprofiletradingacademy.com/

published: 01 Sep 2016

AquaCrop-OS Workshop: Predicting a Better Future for Agriculture and Water Productivity

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of ...

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

Support our videos on Patreon: https://www.patreon.com/user?u=3615911
http://www.answeringmuslims.com/p/jihad.htmlThe Qur'an is filled with violent passages. Yet Muslims assure us that these passages, when read in context, are peaceful. In this video, we examine the historical, immediate, and extended literary contexts of Surah 9:29, which commands Muslims to "fight those who do not believe in Allah."

Support our videos on Patreon: https://www.patreon.com/user?u=3615911
http://www.answeringmuslims.com/p/jihad.htmlThe Qur'an is filled with violent passages. Yet Muslims assure us that these passages, when read in context, are peaceful. In this video, we examine the historical, immediate, and extended literary contexts of Surah 9:29, which commands Muslims to "fight those who do not believe in Allah."

Module 14 - Using Markers to Predict Breeding Values

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN). This module focuses on prediction of breeding values u...

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN). This module focuses on prediction of breeding values using molecular markers in the context of forest trees.

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN). This module focuses on prediction of breeding values using molecular markers in the context of forest trees.

Talk presented at SSCI2014, in Orlando.
Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdfAbstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.

Talk presented at SSCI2014, in Orlando.
Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdfAbstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.

How to Predict Market Direction using Volume and Price | Nigel Hawks

How to get paid to figure out what kind of product or program to sell online
How to create a movement that naturally births a product that everyone wants to buy...

How to get paid to figure out what kind of product or program to sell online
How to create a movement that naturally births a product that everyone wants to buy
How to turn people’s questions, comments, frustrations, and feedback into solid income
How to determine whether a future product will produce the money you need and deserve
InvestorInspiration delivers unbiased investment information by providing a platform for top tier investors to both educate you and inform you about their products. Our primary method of delivering investment information is through webinars featuring multiple industry leading speakers. Find your inspiration today by joining us in our next live webinar or viewing one of our on demand webinar sessions.

How to get paid to figure out what kind of product or program to sell online
How to create a movement that naturally births a product that everyone wants to buy
How to turn people’s questions, comments, frustrations, and feedback into solid income
How to determine whether a future product will produce the money you need and deserve
InvestorInspiration delivers unbiased investment information by providing a platform for top tier investors to both educate you and inform you about their products. Our primary method of delivering investment information is through webinars featuring multiple industry leading speakers. Find your inspiration today by joining us in our next live webinar or viewing one of our on demand webinar sessions.

The Trump Presidency: Last Week Tonight with John Oliver (HBO)

One year after the presidential election, John Oliver discusses what we've learned so far and enlists our catheter cowboy to teach Donald Trump what he hasn't.
...

One year after the presidential election, John Oliver discusses what we've learned so far and enlists our catheter cowboy to teach Donald Trump what he hasn't.
Connect with Last Week Tonight online...
Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight
Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight
Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight
Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight

One year after the presidential election, John Oliver discusses what we've learned so far and enlists our catheter cowboy to teach Donald Trump what he hasn't.
Connect with Last Week Tonight online...
Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight
Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight
Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight
Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight

Webinar: How to use open types and volatility to predict potential trading ranges #marketprofile

Mentorships for those who have the drive to learn to trade patiently and slowly. https://marketprofiletradingacademy.com/were-having-a-blast-with-slack-come-joi...

Mentorships for those who have the drive to learn to trade patiently and slowly. https://marketprofiletradingacademy.com/were-having-a-blast-with-slack-come-join-us/
Call us 541-728-3217 or set 30 minute free consult
Trading is difficult and it can be fun if you can gather yourself and remove your ego and be patient in the process of the market auction coming to you! Time over Price
https://marketprofiletradingacademy.com/

Mentorships for those who have the drive to learn to trade patiently and slowly. https://marketprofiletradingacademy.com/were-having-a-blast-with-slack-come-join-us/
Call us 541-728-3217 or set 30 minute free consult
Trading is difficult and it can be fun if you can gather yourself and remove your ego and be patient in the process of the market auction coming to you! Time over Price
https://marketprofiletradingacademy.com/

published:01 Sep 2016

views:217

back

AquaCrop-OS Workshop: Predicting a Better Future for Agriculture and Water Productivity

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

How to Predict a Company Crisis: Uber, Lego, Marvel Comics | Chris Zook

How did Lego survive a near-total financial ruin? Why is Lyft way more popular that Uber amongst drivers? And how did Marvel gain a second wind some 60 years after it was founded?
Read more at BigThink.com: http://bigthink.com/videos/chris-zook-how-to-predict-a-company-crisis-uber-lego-marvel-comics
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Y'know, overload – just think of all we see of Uber in the paper.
We found four things that we called the “west winds” that hit rapidly growing companies and push them off track, and we called them the west winds.
And one was having revenues growing much faster than you can grow talent.
And just think of Uber, which has had as many as 13 of its top 20 positions unfilled and that has been having difficulty recruiting the most talented women because of some of the cultural issues.
And when your talent is not growing at the same rate as your revenues breakdowns begin to happen.
A second element of the west winds is what we call lack of accountability. And you can look at the culture as it evolved in Uber, which has been well reported, and say, “who is really accountable for the norms and values—deep, soulful values—of a company?” And it’s very easy to neglect that in the context of growth and hype and excitement and celebrity.
Or a third one, or a third of these winds is what we called lost voices from the frontline. And if you look at, for example, the loyalty numbers that you can find all over the internet of Lyft versus Uber drivers, what you find is that the loyalty of Uber drivers is going down, because they’re a little bit frustrated with a lot of the publicity and with some of the behaviors that they’ve observed, and they’re more disoriented about the company.
And so these west winds hit rapidly growing companies. And what we found is that when we looked at these unicorns, we took 26 unicorns about 10 years ago, 10 to 12 years ago, and we traced them. And we found that virtually 100 percent of them—and two-thirds of them had slowed down dramatically and never hit what people wanted them to. Uber would be an example.
And second, we found that in virtually all of the cases the deep inner root cause was not that it was a bad idea in the first place or the market had gone away; It was actually inner breakdowns like this case.
And all of these linked to the founder’s mentality, 1) of linkage to the frontline, 2) maintaining a clear purpose versus overcomplicating what you’re trying to do and becoming greedy and doing too much, and 3) creating mini founder experiences that make people really want to become part of it.
The second crisis is the more predominant crisis, and it’s what we call a stall-out.
So if you think of a company, let’s say like Lego, which since the 1930s was a great founder company through three or four generations. The first did it in wooden blocks. The next brought it to plastic. The next created the business systems. And it was voted the toy of the year by the British toy industry.
And yet the next generation began to say “No, what our core is is the brand. We’re not a toy company.”
And so they went into many, many things that massively complicated the business from theme parks to joint ventures with Steven Spielberg in small theaters to retail endeavors to plastic watches to books, and on and on and on.
And it sucked the energy out of the core and resulted in stall-out to a point where the company had 18 months of cash left.
And the solution to that was to massively reduce complexity, exit all those businesses (by the CEO who courageously went in, Jorgen Vig Knudstorp, well reported in the press) and it gave the company 12 years.
He brought technology into the bricks.
He found out who the core customers were. They didn’t know there were 400,000 people who are obsessed with Lego, they brought them into the design process.
And they did many, many things to actually go back to the essence of what made the company great in the first place, which was the mission of learning, and the customer desire for toy systems that would help children creatively.
And they even did things like take the corporate headquarters (which was in a bright, gleaming new building) and brought it back into the factory and the distribution center.
So that’s an example of stall-out, and that’s an example of a company that then had 12 years of very, very good growth as a result of, in a sense, finding the key to it—where the Founder’s Mentality elements were part of the playbook.

15:30

PREDICT THE FUTURE TWICE | PigCake Tutorials

DO YOU WANT TO LEARN A CLEVER AND INTERESTING SLEIGHT YOU CAN APPLY TO OTHER TRICKS TO FAC...

Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

7:01

Alex Jones Predicted Vegas Attack On Friday

On the Friday edition of The Alex Jones Show, Alex predicted that early October the revolu...

What Does The Word Predict?

Similar words anticipation, prevision, 21 feb 2008our math glossary provides more than simple definitions a link to related lesson is provided for each term in our database the word 'predict' example sentences page 1. It is often, but not always, albert einstein's theory of general relativity could easily be tested as it did produce any effects observable on a terrestrial scale. Word prediction ghotit. Manythings sentences words predict tom did a good job predicting who would win the election ghotit quick spell word prediction helps people with dyslexia and even if topic does not add single new to dictionary, will. Word origin a prediction is what someone thinks will happen. It's a guess, sometimes based on facts or evidence, but not always define predict to say that (something) will might happen in the future words from latin dicere have something do with saying speaking meaning, definition, what is an event action question word ] no one can when disease strike again sentence. However, the 23 dec 2015 if so, then take a look at our collection of predict synonyms. Definition of predict by merriam webster. Predicting and prediction synonyms related words kids. The word 'predict' in example sentences page 1. Au dictionary definition prediction. Predict meaning in the cambridge english dictionary. Pre means before and diction has to do with talking. Predict, prophesy, foresee, forecast mean to know or tell (usually correctly) beforehand what will happen predicting definition, declare in advanceforetell last year, like most sports fans, he did so the winners. Rank popularity for the word 'predict' in verbs frequency #495 predict definition if you an event, say that it will happen. So a prediction is statement about the future. Example sentences with the word predictthey do not represent opinions of yourdictionary. Use predict in a sentence what does mean? Definitions collins english dictionaryoxfordwords blog. Define predict at dictionary browse url? Q webcache. Word forms 3rd person singular present tense predicts, participle predicting a prediction or forecast, is statement about an uncertain event. Prediction dictionary definition vocabulary. A prediction is a forecast, but not only about the weather. Mind reading tricks for the office how to do a word prediction definition math glossary from mathgoodies. Link cite definition of predict in the definitions dictionaryverbs frequency. Do you know how the new year is going to turn out? With tv weatherpersons (just kidding, meteorologists!), word forecast another fine synonym for predict i am not sure if could use in context of a scientific prediction simply saying that something will happen future an 'intelligent' processing feature can alleviate writing since her target did appear this list, student selects next comprehensive list synonyms predicting and prediction, by macmillan dictionary thesaurus reacts ways parents sensitivity rate describes ability ofthe model correctly positives what do next? T

1:30:14

Predicting the Future of the World Economy

When professional stock analysts can't predict the market -- and when monkeys throwing dar...

Description
This presentation will demonstrateMatthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build DeepNeural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep LearningLibrary.
Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

1:28:31

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, indu...

2017 Personality 21: Biology & Traits: Performance Prediction

In this lecture, I talk about the thorny problem of predicting performance: academic, industrial, creative and entrepreneurial); about the practical utility of such prediction, in the business and other environments; about the economic value of accurate prediction (in hiring, placement and promotion) -- which is incredibly high.
Intelligence (psychometrically measured IQ) is the best predictor of performance in complex, ever changing environments. Conscientiousness is the (next) best predictor, particularly in the military, in school and in conservative businesses. Agreeable people make better caretakers; disagreeable people, better disciplinarians and negotiators (within reasonable bounds). Open people are artistic, creative and entrepreneurial. Extraverts are good socially. Introverts work well in isolation. People low in neuroticism have higher levels of tolerance for stress (but may be less sensitive to real signs of danger).
Match the career you pursue to your temperament, rather than trying to adjust the latter. Although some adjustment is possible, there are powerful biological determinants of the five personality dimensions and IQ (particularly in environments where differences are allowed to flourish).
To support this channel: Patreon: https://www.patreon.com/jordanbpeterson
Other relevant links:
Personality analysis: www.understandmyself.com
Self Authoring: http://selfauthoring.com/
Jordan Peterson Website: http://jordanbpeterson.com/
Podcast: http://jordanbpeterson.com/jordan-b-p...ReadingList: http://jordanbpeterson.com/2017/03/gr...
Twitter: https://twitter.com/jordanbpeterson

Qur'an in Context 1: "Fight Those Who Do Not Believe" (9:29)

Support our videos on Patreon: https://www.patreon.com/user?u=3615911
http://www.answeringmuslims.com/p/jihad.htmlThe Qur'an is filled with violent passages. Yet Muslims assure us that these passages, when read in context, are peaceful. In this video, we examine the historical, immediate, and extended literary contexts of Surah 9:29, which commands Muslims to "fight those who do not believe in Allah."

53:50

Module 14 - Using Markers to Predict Breeding Values

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics ...

Module 14 - Using Markers to Predict Breeding Values

This is the 14th module in a series of 16 developed by the Conifer Translational Genomics Network (CTGN). This module focuses on prediction of breeding values using molecular markers in the context of forest trees.

1:30:14

Predicting the Future of the World Economy

When professional stock analysts can't predict the market -- and when monkeys throwing dar...

How to predict students' performance?

Talk presented at SSCI2014, in Orlando.
Download paper from: http://personal.ee.surrey.ac.uk/Personal/Norman.Poh/data/poh_gradcert.pdfAbstract: Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.

41:41

How to Predict Market Direction using Volume and Price | Nigel Hawks

How to get paid to figure out what kind of product or program to sell online
How to create...

How to Predict Market Direction using Volume and Price | Nigel Hawks

How to get paid to figure out what kind of product or program to sell online
How to create a movement that naturally births a product that everyone wants to buy
How to turn people’s questions, comments, frustrations, and feedback into solid income
How to determine whether a future product will produce the money you need and deserve
InvestorInspiration delivers unbiased investment information by providing a platform for top tier investors to both educate you and inform you about their products. Our primary method of delivering investment information is through webinars featuring multiple industry leading speakers. Find your inspiration today by joining us in our next live webinar or viewing one of our on demand webinar sessions.

23:51

The Trump Presidency: Last Week Tonight with John Oliver (HBO)

One year after the presidential election, John Oliver discusses what we've learned so far ...

The Trump Presidency: Last Week Tonight with John Oliver (HBO)

One year after the presidential election, John Oliver discusses what we've learned so far and enlists our catheter cowboy to teach Donald Trump what he hasn't.
Connect with Last Week Tonight online...
Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight
Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight
Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight
Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight

Webinar: How to use open types and volatility to predict potential trading ranges #marketprofile

Mentorships for those who have the drive to learn to trade patiently and slowly. https://marketprofiletradingacademy.com/were-having-a-blast-with-slack-come-join-us/
Call us 541-728-3217 or set 30 minute free consult
Trading is difficult and it can be fun if you can gather yourself and remove your ego and be patient in the process of the market auction coming to you! Time over Price
https://marketprofiletradingacademy.com/

1:18:20

AquaCrop-OS Workshop: Predicting a Better Future for Agriculture and Water Productivity

Speakers:
Tim Foster, Lecturer in Water-Food Security, University of Manchester; United Ki...

AquaCrop-OS Workshop: Predicting a Better Future f...

What if... we could predict earthquakes?...

It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

Britain must prove Russia’s involvement in the poisoning of the former double agent Sergei Skripal in the UK or apologise, the Kremlin has said. “Sooner or later these unsubstantiated allegations will have to be answered for. either backed up with the appropriate evidence or apologised for,” the presidential spokesman, Dmitry Peskov, said on Monday ... Sergei Skripal. Russia expels 23 UK diplomats as row deepens. Read more ... ....

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Fourth's predictions for the top anticipated trending topics at these industry events include. ... Restaurants and hotel operators use dozens of tech vendors to manage everything from food orders, to accounting, to staff and getting actionable insight from all this data in context will be key to driving profits and guest loyalty ... It is predicted that food delivery sales in the U.S....

After several hours building bases and trying not to get shot, my overall takeaway is that Fortnite iOS holds up well against its console counterparts in terms of performance — and even has some unique benefits when played — but does come with the predictable drawbacks of playing a shooter on a phone ...All of this, of course, has to be understood in the context of that initial point, which is impossible to ignore ... ....

Ho ... To what degree is Xi set to become the all-powerful ruler many observers predict? ... But how should this New Era be viewed in the context of modern Chinese history, and what might it reveal about the nature of Xi's power? ... Political scientists stopped predicting the CPC's collapse and began asking instead why the one-party authoritarian state has been so resilient, and whether the period of reform and opening up had come to an end ... ....

Su is in the United Kingdom for the China in Context book festival in London’sChinatown, which celebrates the best writers and writing with a China connection ... He hopes events such as the China in Context festival will help break down barriers in communication and generate interest in Chinese books....

Prediction? Fans have spotted that Stephen Hawking nods to Albert Einstein an episode of The Simpsons which aired in 1999, 19 years before the physicist would die on the latter German scientist's day of birth ... Fans have already tied together Wednesday 14 March as the date of Hawking's death (left) and the day that Albert Einstein was born (right) but did The Simpsons predict their greater link years ahead of time?....

Food and Drug Administration granted the De Novo request for Edwards' Acumen Hypotension PredictionIndex (HPI) software. The company will initiate a targeted launch of this first-of-its-kind technology that leverages predictive analytics to alert clinicians to address potential hypotension, or low blood pressure, before it occurs in their surgical patients....

Since the beginning of recorded history there have been end of the world predictions... The book is a point-by-point takedown of the predictions of disaster made by the climate change movement, none of which have materialized, but when one is part of a cult, facts don’t matter ... “We meteorologists are well aware of how limited our ability is to predict the weather....

It ended after 82 days. Or 13 games. Whichever way it is measured, Alvaro Morata’s wait for his 13th Chelsea goal was a lengthy affair. Until he found the net in Sunday’s FA Cup win at Leicester City, his 2018 had produced seven yellow cards, one red and no goals ... They identified the same men ... There has been a predictable element to Morata’s struggles ... perhaps Chelsea just traded down, certainly in the context of English football....

In this week’s episode of History Respawned, a podcast where historians discuss games that deal with history in some way, host John Harney is joined by Bob Whitaker and guest Robert Greene II in order to work through the historical context that The New Colossus is borrowing from in order to create its fictional Nazi-dominated 20th century....

In the March 15 edition of The Athesn NEWS, a political cartoon by the talented Sandy Plunkett raised an interesting issue ...Predictably, George and Jerry’s pilot is rejected ... So it should be in real life ... Ultimately, the security of women’s bodies, in the context of their own sexualization, is not solely up to them – just like every other human, a woman’s safety is also dependent on the people and the environment around her ... ....

In fact, they predict, it's not likely to leave anything white in its wake ...Little or no accumulation of snow is expected, and that will hold true through the day Tuesday when a rain/snow mix is predicted by the federal forecasters The daytime high is forecast at a chilly 38 ... with less than a tenth of an inch of precipitation predicted....